Wave 1: umbrella backport from the Strix Halo repo — patch 0003, GPU tools, porting docs, strix-halo profile, governance

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,11 @@
+---
+name: Bug report
+about: Something broke on your AMD GPU
+title: "bug: "
+labels: bug
+---
+**Hardware / stack** (paste `scripts/00-check-env.sh` output or
+docs/results/environment.json): 
+**Exact command:** 
+**Full log** (pastebin/gist if long): 
+**What you expected vs what happened:** 

.github/ISSUE_TEMPLATE/validation_report.md

@@ -0,0 +1,11 @@
+---
+name: Validation report
+about: Share results from YOUR hardware (successes AND failures)
+title: "validation: <GPU> / ROCm <version>"
+labels: validation
+---
+**GPU / ROCm / torch:** 
+**`00-check-env.sh` output:** 
+**Summary table** (`python3 scripts/summarize_results.py`): 
+**Receipts** (docs/results/… if you can share): 
+**Anything that failed, with the log:** 

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,10 @@
+**What & why** (one paragraph; link the spec/design doc if any): 
+
+**Evidence** (receipts under docs/results/, or `verified on: <gfx>` +
+link for ports from sibling repos — see CONTRIBUTING): 
+
+**Gates** (must both be green):
+- [ ] `python3 -m pytest tests/ -q`
+- [ ] `shellcheck scripts/*.sh scripts/lib/common.sh docs/hardware/strix-halo/hsa_fix.sh`
+
+**If ported from SenseNova-U1.5-ROCm-8060S:** source commit SHA:

.github/workflows/ci.yml

@@ -19,4 +19,4 @@ jobs:
       - name: ShellCheck the pipeline scripts
         run: |
           sudo apt-get install -y shellcheck -q
-          shellcheck scripts/*.sh scripts/lib/common.sh
+          shellcheck scripts/*.sh scripts/lib/common.sh docs/hardware/strix-halo/hsa_fix.sh

CHANGELOG.md

@@ -16,6 +16,28 @@ and the project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.
   to PR head e2f2c865; V1–V4 on-model verification (list input fixed,
   tuple output byte-identical) — receipts
   docs/results/validation/interleave-image-size/.
+- Patch 0003: optional torch.compile/cudagraph safety (env-gated
+  `SENSENOVA_COMPILE*`); ported from the Strix Halo sibling repo —
+  verified on gfx1151 (V1–V8 + quality bench there); gfx1100 receipt
+  pending, compile stays opt-in.
+- `scripts/gpu_monitor.py`: sample VRAM + GTT while a workload runs
+  (JSONL + peak summary) — GTT visibility is what evidences APU
+  GTT-spill runs.
+- `scripts/rocm_check.py`: env sanity check (HIP build, wheel gfx
+  coverage, bf16 matmul + SDPA smoke, optional GTT-spill alloc test).
+- Hardware profile `docs/hardware/strix-halo/` (EN+CN): GTT-spill
+  memory model, HSA runtime shim (`hsa_fix.sh`, now CI-shellchecked),
+  gfx1151 wheel guidance, AOTriton hd72 caveat — the umbrella's first
+  APU profile, ported from the 8060S repo.
+- `docs/porting.md` + `docs/porting_CN.md`: generic ROCm porting notes,
+  split from the 8060S repo's porting doc (APU-specific depth moved to
+  the strix-halo profile).
+- `docs/upstream/aotriton-54.md`: upstream tracking entry for the
+  AOTriton hd=72 silent-wrong-output bug (issue #54, fix unmerged);
+  verified on gfx1151, evidence in the 8060S repo.
+- Governance: Code of Conduct, `SECURITY.md`, bug/validation issue
+  templates, PR template (evidence gates), CONTRIBUTING cross-repo
+  section (graduation model), README/README_CN hardware-coverage table.
 
 ### Changed
 - pytorch#194498 root cause verified on-host: SDPA fused-backend launch

CODE_OF_CONDUCT.md

@@ -0,0 +1,22 @@
+# Code of Conduct
+
+## Our pledge
+Be evidence-first about claims and kind to people. We welcome
+contributors of every background and skill level.
+
+## Standards
+**Encouraged:** citing receipts for claims, asking questions, reporting
+both successes and failures, reviewing others' work respectfully.
+**Unacceptable:** harassment or discriminatory language, hostile
+responses to newcomers, presenting unverified numbers as measured,
+doxxing, spam.
+
+## Enforcement
+Report violations by opening a private GitHub security advisory
+("Report a vulnerability" → select "not applicable") or contacting a
+maintainer via GitHub. Maintainers will respond within 7 days and may
+remove comments/commits/ban repeat offenders.
+
+## Attribution
+Adapted in spirit from the Contributor Covenant 2.1.
+本行为准则同样适用于中文社区讨论；违反行为可同样通过上述渠道举报。

CONTRIBUTING.md

@@ -51,3 +51,25 @@ re-runs a single block for a minimal reproduction.
 - Scripts: `set -euo pipefail`, a `usage()` with `--help`, clear `[project]`
   log prefixes — follow the existing scripts.
 - Numbers in docs: cite the receipt file they came from.
+
+## Contributing between the two repos
+
+This repo is the **umbrella** (hardware-generic assets + hardware
+profiles). [SenseNova-U1.5-ROCm-8060S](https://github.com/AIwork4me/SenseNova-U1.5-ROCm-8060S) is the **Strix Halo lab**:
+APU-first experiments live there; verified work graduates here.
+
+**8060S → umbrella (graduation).** A change graduates when it is
+① hardware-independent or absorbed as a hardware profile, ② verified on
+gfx1151 with evidence in the 8060S repo, ③ passes this repo's offline
+CI, ④ documented EN+CN, ⑤ not a duplicate of an existing tool here.
+Open a PR labelled `backport`; the commit carries
+`Ported from …@<sha>` and the claim is labelled
+`verified on: gfx1151` until a gfx1100 receipt lands here.
+
+**Umbrella → 8060S (flow-down).** Changes to shared surfaces
+(`patches/`, `scripts/`) are cherry-picked by the 8060S repo as
+needed; that repo records `Merged-upstream: <sha>`.
+
+**Evidence across repos.** `receipt.py` records `hardware=<gfx>` on
+every run; cross-hardware claims must link the producing repo's
+evidence.

README.md

@@ -36,6 +36,17 @@ bash scripts/quickstart.sh          # t2i | vqa | edit | interleave
 [Troubleshooting](docs/getting-started.md#troubleshooting) ·
 [Contributing](CONTRIBUTING.md)
 
+## Hardware coverage
+
+| hardware | status | where |
+|---|---|---|
+| gfx1100 (RDNA3 dGPU) | fully validated — receipts in [docs/results/](docs/results/) | this repo |
+| gfx1151 (Strix Halo APU) | fully validated — evidence in the [8060S repo](https://github.com/AIwork4me/SenseNova-U1.5-ROCm-8060S); profile: [docs/hardware/strix-halo/](docs/hardware/strix-halo/) | this repo + Strix Halo lab |
+
+Other AMD GPUs: the pipeline is hardware-generic (see
+[Porting Notes](docs/porting.md)) — validation reports welcome
+(issue template `validation_report`).
+
 ## Showcase: movie-poster style
 
 Generated on the reference gfx1100 host with this repo's pipeline

README_CN.md

@@ -27,11 +27,25 @@
   [SenseNova-U1#260](https://github.com/OpenSenseNova/SenseNova-U1/pull/260)）与
   ROCm SDPA 融合后端崩溃（补丁 0002，[pytorch#194498](https://github.com/pytorch/pytorch/issues/194498)、
   [SenseNova-U1#261](https://github.com/OpenSenseNova/SenseNova-U1/issues/261)）
+- 🧪 **gfx1151（Strix Halo APU）同样全量验证**：证据见姊妹仓库
+  [SenseNova-U1.5-ROCm-8060S](https://github.com/AIwork4me/SenseNova-U1.5-ROCm-8060S)
+  （Strix Halo 实验室），硬件配置在本仓库
+  [docs/hardware/strix-halo/](docs/hardware/strix-halo/)
 
 ```bash
 bash scripts/quickstart.sh          # 可选任务: t2i | vqa | edit | interleave
 ```
 
+## 硬件覆盖
+
+| 硬件 | 状态 | 位置 |
+|---|---|---|
+| gfx1100（RDNA3 独显） | 完全验证 —— 收据见 [docs/results/](docs/results/) | 本仓库 |
+| gfx1151（Strix Halo APU） | 完全验证 —— 证据见 [8060S 仓库](https://github.com/AIwork4me/SenseNova-U1.5-ROCm-8060S)；配置：[docs/hardware/strix-halo/](docs/hardware/strix-halo/) | 本仓库 + Strix Halo 实验室 |
+
+其他 AMD GPU：本管线与硬件无关（见[移植指南](docs/porting.md)）——
+欢迎提交验证报告（issue 模板 `validation_report`）。
+
 ## 效果展示：电影海报风格
 
 以下海报由本仓库管线在 gfx1100 参考机上生成

SECURITY.md

@@ -0,0 +1,17 @@
+# Security Policy
+
+## Supported versions
+The tip of `main` only.
+
+## Reporting a vulnerability
+Please do NOT open a public issue. Use GitHub's private vulnerability
+reporting ("Security" tab → "Report a vulnerability"). Include
+reproduction steps and affected components (scripts/, patches/, docs/
+pipeline). Expect a response within 7 days.
+
+## Scope
+This repo ships shell/python scripts and upstream patches; it does not
+serve anything to the network. Model weights come from upstream
+ModelScope — weight integrity is verified by
+configs/artifact-manifest.json SHA256s (any tampering report is
+welcome but belongs upstream).

docs/getting-started.md

@@ -2,6 +2,9 @@
 
 From a bare Linux host with an AMD GPU to your first SenseNova-U1.5 output.
 
+For how this port works (wheels, attention backend, memory tiers):
+[Porting Notes](porting.md) · [移植笔记](porting_CN.md)
+
 ## What you need
 
 | Requirement | Why | Reference host |

docs/hardware/strix-halo/README.md

@@ -0,0 +1,93 @@
+# Hardware Profile: Strix Halo (Radeon 8060S iGPU, gfx1151)
+
+verified on: gfx1151 — Ryzen AI MAX+ PRO 395, ROCm 7.2.1 system +
+torch 2.12.0+rocm7.14.0 wheels. Full evidence corpus (V1–V8 speedup,
+quality bench, determinism, tier tests):
+[SenseNova-U1.5-ROCm-8060S](https://github.com/AIwork4me/SenseNova-U1.5-ROCm-8060S).
+gfx1100 receipts live in [docs/results/](../../results/) here.
+Generic background: [porting notes](../../porting.md).
+
+## Why full bf16 fits a "32 GB" iGPU — GTT spill
+
+On AMD APUs the GPU shares physical memory with the CPU. The driver
+exposes two pools: dedicated **VRAM** (BIOS carve-out, e.g. 32 GB on the
+reference machine) and **GTT** (the rest of system memory the GPU may
+map, e.g. 80 GB). ROCm's allocator can allocate past dedicated VRAM by
+spilling into GTT, so `torch.empty(50 GiB, device="cuda")` succeeds.
+Since both pools are the same physical LPDDR5X on package, the penalty
+is modest — in the 8060S repo's suite runs the allocator put ~1.2 GiB in
+VRAM and up to 41.6 GiB in GTT (112 GiB total GPU-addressable), with
+full-mode peaks ≤ 42.9 GiB, so ~45+ GiB addressable works in practice.
+
+`scripts/rocm_check.py --alloc-test 48` verifies exactly this spill
+(48 GiB allocation; archived as `evidence/stack714/env_check.json` in
+the 8060S repo). This is what makes `--vram_mode full` (entire ~50 GB
+checkpoint GPU-resident, no layer offload) possible on a "32 GB" GPU.
+Note when choosing tiers on the 7.14 stack: `full` is also the fastest
+tier there — do not override to `fast`/`balanced` for speed (8060S
+PERFORMANCE.md, 2026-08-25 correction; tiers remain the tool for VRAM
+capacity on smaller GPUs).
+
+## HSA runtime pitfall (torch +rocm7.0 wheels, driver ≥ 7.1)
+
+Official `torch 2.10.0+rocm7.0` wheels bundle a ROCm 7.0 HSA runtime
+that segfaults on gfx1151 (Strix Halo) when the kernel driver is from
+ROCm ≥ 7.1: crash in `GpuAgent::QueueCreate` → `ReleaseQueueMainScratch`
+while freezing the first code object — every GPU op dies. Fix: preload
+the system ROCm runtime
+(`LD_PRELOAD=/opt/rocm/lib/libhsa-runtime64.so`), which always matches
+the kernel driver.
+
+This profile ships the shim as [hsa_fix.sh](hsa_fix.sh) — source it
+(not execute) and call `hsa_fix_apply "$PY" "$REPO_ROOT"`; it exports
+`LD_PRELOAD` only when the system runtime is NEWER than the wheel's
+bundled one, so machines where the bundled runtime already works (or is
+newer) are untouched. The recommended 7.14 wheels run bare and never
+trigger it.
+
+## Wheel guidance for gfx1151
+
+- **Default (recommended):** AMD official multi-arch
+  `torch 2.12.0+rocm7.14.0` from
+  `https://repo.amd.com/rocm/whl-multi-arch/` — verified bare-run on
+  gfx1151 (no HSA preload needed) and faster than the torch-2.10 build
+  in the 8060S A/B (167.9 s vs 232.1 s for a 1024×1024 t2i in `fast`
+  mode; both sessions were contended, so treat the magnitude as
+  indicative). These wheels bundle their own ROCm userspace — a system
+  ROCm mainly provides the kernel-driver tooling (`rocminfo`,
+  `rocm-smi`).
+- **Fallback:** official PyTorch rocm7.0 index
+  (`https://download.pytorch.org/whl/rocm7.0`, `torch 2.10.0+rocm7.0`;
+  covers gfx942/950/1100/1101/1151/1200) — on gfx1151 it needs the HSA
+  preload above.
+- **Last resort:** AMD arch-specific nightlies
+  (`https://rocm.nightlies.amd.com/v2/<gfx>/`) for arches the official
+  wheels miss.
+
+In this repo `scripts/01-setup-venv.sh` installs from a single index —
+point `PY_INDEX_ROCM` at the source you want; the deeper
+wheel-has-kernels-for-your-gfx check is `scripts/rocm_check.py`
+(see [porting notes §4](../../porting.md#4-pytorch-wheel-matrix-known-rocm-wheel-sources)).
+
+## AOTriton caveat (hd=72 ViT heads)
+
+On gfx1151 with torch 2.12+rocm7.14, AOTriton flash SDPA is *silently
+wrong* for head_dim=72 with seq>1024, **non-causal only** (causal
+attention at the same shape is correct) — errors drift run-to-run,
+typically 16–730% relative error, dominantly finite garbage with
+sporadic NaN in ~1/5 of calls. SenseNova's own attention shapes
+(head_dim 64/128) are unaffected, but disable AOTriton for a foreign
+model with a 16-head/1152-hidden ViT. Root cause: upstream AOTriton
+[issue #54](https://github.com/ROCm/aotriton/issues/54) (missing
+`out_dtype=tl.float32` in the fwd kernel); a fix exists (commit
+`8232d69672`) but is unmerged in every release so far — tracked in
+[docs/upstream/aotriton-54.md](../../upstream/aotriton-54.md).
+
+## Companion repo
+
+The daily-driver run scripts, one-command quickstart, and the full
+evidence corpus live in the
+[8060S repo](https://github.com/AIwork4me/SenseNova-U1.5-ROCm-8060S) —
+the Strix Halo lab where these numbers were measured. Experiments there
+graduate into this umbrella via PR when they meet the graduation
+criteria ([CONTRIBUTING.md](../../../CONTRIBUTING.md)).

docs/hardware/strix-halo/README_CN.md

@@ -0,0 +1,82 @@
+# 硬件 Profile：Strix Halo（Radeon 8060S 核显，gfx1151）
+
+verified on: gfx1151 —— Ryzen AI MAX+ PRO 395，系统 ROCm 7.2.1 +
+torch 2.12.0+rocm7.14.0 wheel。完整证据集（V1–V8 提速、质量评测、
+确定性、档位测试）：
+[SenseNova-U1.5-ROCm-8060S](https://github.com/AIwork4me/SenseNova-U1.5-ROCm-8060S)。
+gfx1100 凭据在本仓库 [docs/results/](../../results/)。
+通用背景见[移植笔记](../../porting_CN.md)。
+
+## 为什么"32 GB"核显装得下完整 bf16 —— GTT 溢出
+
+AMD APU 的 GPU 与 CPU 共享物理内存。驱动暴露两个池：专用 **VRAM**
+（BIOS 挖出，参考机型为 32 GB）与 **GTT**（GPU 可映射的其余系统内存，
+如 80 GB）。ROCm 分配器越过专用 VRAM 时会溢出到 GTT，因此
+`torch.empty(50 GiB, device="cuda")` 能成功。两个池都是同一颗封装内
+的 LPDDR5X 物理内存，代价不大——8060S 仓库套件运行期间，分配器把
+~1.2 GiB 放进 VRAM、最多 41.6 GiB 进 GTT（GPU 可寻址合计 112 GiB），
+full 档峰值 ≤ 42.9 GiB，实践上 ~45+ GiB 可寻址即可。
+
+本仓库的 `scripts/rocm_check.py --alloc-test 48` 正是验证这一溢出
+（48 GiB 分配；8060S 仓库存证 `evidence/stack714/env_check.json`）。
+这就是"32 GB 显存"也能跑 `--vram_mode full`（约 50 GB 权重全驻留
+GPU、不做 layer offload）的原因。7.14 栈上选档提示：`full` 同时也是
+最快档——不要为提速改选 `fast`/`balanced`（8060S PERFORMANCE.md
+2026-08-25 更正；档位仍是小显存 GPU 的容量工具）。
+
+## HSA 运行时的坑（torch +rocm7.0 wheel，驱动 ≥ 7.1）
+
+官方 `torch 2.10.0+rocm7.0` 轮子自带 ROCm 7.0 的 HSA 运行时，在
+gfx1151（Strix Halo）+ ROCm ≥ 7.1 内核驱动上冻结首个 code object 时
+（`GpuAgent::QueueCreate` → `ReleaseQueueMainScratch`）段错误——任何
+GPU 算子都跑不起来。解法：预加载系统 ROCm 运行时
+（`LD_PRELOAD=/opt/rocm/lib/libhsa-runtime64.so`），它必然与内核驱动
+匹配。
+
+本 profile 以 [hsa_fix.sh](hsa_fix.sh) 携带该 shim——source（而非
+执行）本文件后调用 `hsa_fix_apply "$PY" "$REPO_ROOT"`；仅当系统运行时
+比 wheel 自带的**更新**时才 export `LD_PRELOAD`，自带运行时本来就
+正常（或更新）的机器不受影响。推荐的 7.14 wheel 裸跑，不会触发。
+
+## gfx1151 wheel 指南
+
+- **默认（推荐）**：AMD 官方多架构 `torch 2.12.0+rocm7.14.0`，
+  索引 `https://repo.amd.com/rocm/whl-multi-arch/`——gfx1151 实测
+  裸跑（无需 HSA 预加载），且在 8060S A/B 中快于 torch-2.10 构建
+  （1024×1024 t2i fast 档 167.9 s vs 232.1 s——当年会话存在争用，
+  幅度仅供参考）。这类 wheel 自带 ROCm 用户态——系统 ROCm 主要提供
+  内核驱动工具（`rocminfo`、`rocm-smi`）。
+- **回退**：官方 PyTorch rocm7.0 索引
+  （`https://download.pytorch.org/whl/rocm7.0`，`torch 2.10.0+rocm7.0`；
+  覆盖 gfx942/950/1100/1101/1151/1200）——gfx1151 上需上面的 HSA
+  预加载。
+- **兜底**：AMD 按架构 nightly
+  （`https://rocm.nightlies.amd.com/v2/<gfx>/`），用于官方 wheel 缺
+  架构时。
+
+本仓库的 `scripts/01-setup-venv.sh` 从单一索引安装——用
+`PY_INDEX_ROCM` 指向你想要的来源；更深入的"wheel 是否带你 gfx 内核"
+检查是 `scripts/rocm_check.py`
+（见[移植笔记 §4](../../porting_CN.md#4-pytorch-wheel-矩阵已知-rocm-wheel-来源)）。
+
+## AOTriton 告警（hd=72 ViT 头）
+
+gfx1151 + torch 2.12+rocm7.14 下，AOTriton flash SDPA 在 head_dim=72
+且 seq>1024、**仅非因果注意力**时**静默输出错误值**（同形状因果注意力
+正常）——误差逐次漂移，典型相对误差 16~730%，以有限错误值为主、约
+1/5 次调用出现 NaN。SenseNova 自身的注意力形状（head_dim 64/128）
+不受影响；但若复用环境跑 16 头/hidden 1152 的外部 ViT 模型，请对其
+关闭 AOTriton。根因：上游 AOTriton
+[issue #54](https://github.com/ROCm/aotriton/issues/54)（fwd 内核缺
+`out_dtype=tl.float32`）；修复 commit `8232d69672` 已存在但至今未合并
+进任何发布——追踪详情见
+[docs/upstream/aotriton-54.md](../../upstream/aotriton-54.md)。
+
+## 配套仓库
+
+日常驱动脚本、一条命令 quickstart 与完整证据集都在
+[8060S 仓库](https://github.com/AIwork4me/SenseNova-U1.5-ROCm-8060S)
+——即 Strix Halo 实验室，上述数字都在那里测得。实验在那里达到毕业
+标准（graduation criteria，
+[CONTRIBUTING.md](../../../CONTRIBUTING.md)）后，通过 PR 毕业进入
+本 umbrella 仓库。